AI Reference Signal Density Control for Channel Estimation
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Solution Overview
Problem
Existing wireless communication systems face challenges in efficiently utilizing reference signal (RS) resources, leading to high overhead and reduced network performance due to the need for high-density RSs for channel estimation, which consumes significant radio resources.
Innovation Solution
Implementing AI-based RS processing techniques that utilize both high and low-density RSs for training neural networks, allowing for joint training and channel estimation with reduced overhead by using semi-supervised learning with labeled and unlabeled data sets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If high-density reference signals are used for channel estimation, then channel estimation accuracy is improved, but radio resource overhead increases
Solution Approach 1:
The patent applies parameter changes by utilizing reference signals with different densities (first density and second density) instead of uniformly high-density signals. The system dynamically adjusts which density to use based on channel conditions, enabling accurate channel estimation while reducing overall radio resource overhead through selective signal density deployment.
Solution Approach 2:
The patent implements partial action by using a combination of high-density and low-density reference signals rather than universally deploying high-density signals throughout all communication scenarios. This partial application of high-density signals only where necessary maintains estimation accuracy while avoiding excessive resource consumption in scenarios where lower density suffices.
2Productivity
If multiple reference signal densities are deployed, then resource utilization is optimized, but system complexity increases
Solution Approach 1:
The patent applies dynamics by implementing adaptive reference signal selection where the system dynamically determines which density (first or second) to use based on real-time channel conditions and communication requirements. This dynamic adjustment enables optimized resource utilization across varying network conditions while managing system complexity through algorithmic adaptation rather than fixed complex infrastructure.
Solution Approach 2:
The patent segments the reference signal deployment into distinct first and second density layers, each serving specific purposes. The first density provides detailed estimation where needed, while the second density provides coverage where sufficient. This segmentation allows the system to manage complexity by treating different density levels as separate, manageable components with defined roles.
Data Source
AI summary
Aspects described herein relate to performing first signal measurements of reference signals (RSs) received over a first set of one or more RS occasions based on a first RS density, performing second signal measurements of RSs received over a second set of RS occasions based on a second RS density, and providing the first signal measurements identified as a first type of signal measurements and the second signal measurements identified as a second type of signal measurements for training a neural network, such as for channel state information reference signal (CSI-RS) transmission or channel estimation.